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5 results for “anti-predator strategies”
Simulation Dataset: Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies
<p>This is a supplementary simulation dataset to reproduce Fig. 3C,D of the manuscript "Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies" by Bartashevich et al.</p> <p>The zip folder contains the following 3 files in h5 format: front attack (out_Npred1_pred_angle0.0.h5), side attack (<span>out_Npred1_pred_angle1.5707963267948966.h5), </span><span>back attack (out_Npred1_pred_angle3.141592653589793.h5).</span></p> <p>Each file has the following "keys": KeysViewHDF5 ['circ_seg', 'end', 'endD', 'end_PosVel', 'fount', 'part', 'partD', 'pavas', 'pred', 'predD', 'start', 'start_fountain', 'start_pred', 'swarm', 'swarm_pred0', 'swarm_predD'].</p> <p>The key necessary to reproduce Fig. 3C,D of the aforementioned paper is "fount" (<HDF5 dataset "fount": shape (40, 1200, 100, 8), type "<f8">). Namely, "fount" data array consists of 40 simulation runs, 1200 time points, 100 agents, and 8 metrics. The metric with index "0" depicts the value of the Euclidean distance from the agent <em>i</em> to the simulated predator. The metric with index "1" depicts the value of the position angle (theta 1 in rad) of the agent <em>i</em> relative to the simulated predator. The metric with index "2" depicts the value of the flee angle (theta 2 in rad) of the agent <em>i</em> relative to the simulated predator.</p> <p>To estimate the start and the end of the fountain evasion, one can use the following script in Python:</p> <p>import numpy as np</p> <p>m = h5py.File(filename, "r")<br><br>for key in m.keys():<br> print(key)</p> <p>fount_runs = m[key]["fount"]</p> <p>for j in range(40):<br> fnt_start[j] = np.where(fount_runs[j, 0:1200, 0:100,5]==1)[0][0] <br> fnt_end[j] = np.where(fount_runs[j, 0:1200, 0:100,5]==1)[0][-1]</p>
Empirical Dataset: Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies
<h3>Description of the data and file structure</h3> <p>The files contain the source data for Fig. 1 and Fig. 3A,B of the manuscript "Collective anti-predator escape manoeuvres through optimal attack and avoidance strategies" by Bartashevich et al.</p> <h4>Files and variables</h4> <h5>File: Fountain_Fish_coordinates.zip</h5> <p><strong>Description:</strong> </p> <p>The zip file contains 30 folders, each containing information on one predator attack and respective prey evasion. Each folder is named according to the drone ID used for the filming (e.g., DJI _1, DJI _2, DJI _3) and the respective frame number (e.g., f930) from the video recording. </p> <h5>File naming</h5> <p>Each folder contains JPG and CSV files. </p> <p>The JPG files show the image from the footage at the corresponding frame indicated in the files' name (e.g., frame_001_im).</p> <p>There are 2 types of CSV files. Files with the name 'polygon.csv' contain coordinates (in pixels) of points (x, y) defining the polygon outlining the prey school at the particular frame as indicated in the files' name (e.g., frame001) and corresponding to the image in the JPG file with the same frame number. Files with the name 'sardines_and_marlin.csv' contain coordinates (in pixels) of points (x, y), defining the head (columns 1 and 2) and the dorsal fin (columns 3 and 4) of single sardine individuals (by rows), and of the respective attacking marlin: marlin's head (columns 5 and 6), marlin's dorsal fin (columns 7 and 8), and marlin's tip of the bill (columns 9 and 10). These coordinates correspond to the respective image with the same frame number.</p>
Nightly selection of resting sites and group behavior reveal anti-predator strategies in giraffe
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Data from: Unpredictable movement as an anti-predator strategy
Prey animals have evolved a wide variety of behaviours to combat the threat of predation, and these have been generally well studied. However, one of the most common and taxonomically widespread antipredator behaviours of all has, remarkably, received almost no experimental attention: so-called 'protean' behaviour. This is behaviour which is sufficiently unpredictable to prevent a predator anticipating in detail the future position or actions of its prey. In this study, we used human 'predators' participating in 3D virtual reality simulations to test how protean (i.e. unpredictable) variation in prey movement affects participants' ability to visually target them as they move (a key determinant of successful predation). We found that targeting accuracy was significantly predicted by prey movement path complexity, although, surprisingly, there was little evidence that high levels of unpredictability in the underlying movement rules equated directly to decreased predator performance. Instead, the specific movement rules differed in how they impacted on targeting accuracy, with the efficacy of protean variation in one element depending on the values of the remaining elements. These findings provide important insights into the understudied phenomenon of protean antipredator behaviour, which are directly applicable to predator-prey dynamics within a broad range of taxa.
Data from: Unpredictable movement as an anti-predator strategy
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.